Digital Rights and the Cost of “Lousy Record Keeping”

Every year, publishers pay millions of dollars in legal fees to settle copyright infringement suits for misusing licensed images and other digital assets. One legal blogger has written extensively about the problem and has noted that, overall, many publishers seem to have “lousy record keeping”.

Publishers are not alone in having problems. From what I’ve seen, most companies do a poor job tracking the rights attached to their digital assets. The root cause of this widespread problem is that most Digital Asset Management (DAM) systems provide only the most rudimentary tools for tracking rights. For each asset, users can usually enter the name of the copyright holder and the credit line, but more fine-grained details are lost. How many times can an image be reused and how many times has it been used? What publishing channels are permitted? Which geographic regions and languages are allowed?

Tracking all your permissions information requires modeling your licenses and then configuring your DAM to hold the most important data points. This is not easy to do. Indeed, configuring your DAM to track this information may not even be possible because these systems are often restricted to simple name-value pairs and cannot handle date ranges, multi-part values, hierarchical metadata, or conditional terms.

Consider the complexity of licensing a Rights Managed image from Getty. If you want to use an image in an electronic book, the Getty license contains three distinct data points: the format of the publication, the start date of the license, and the duration of the license. However, if you want to use the same image in a print textbook, the license now contains seven data points, including the placement of the image within the book, and whether the image will be used in a first or subsequent edition. The terms change again if you wish to use the image on a textbook cover. In this case, you must track the allowed size of the image, as well as how many copies of the book will be circulated.

Licensing an image for use in an electronic book, a textbook interior, and a textbook cover requires three different tables.

As the above illustration indicates, you would need to create three different tables in your database to hold the terms of these three different licenses from Getty. And of course, it goes without saying that every stock photo house has different terms, none of which fit neatly into any of the same tables. No wonder most publishers have trouble with their record keeping.

The good news is that semantic web technologies can help overcome many of these technical problems. Semantic databases do not force data into rigid tables. Instead, these databases represent information as a graph of interconnected facts. When you get a new license, you don’t need to build a new table. Instead, you simply add a new graph of facts that can then be queried, aggregated, and updated.

Graphs representing the licenses for an image used in an electronic book and the interior of a textbook.

All of this is rather abstract, but the bottom line is that semantics allow you to capture your licenses in a much more nuanced way than ever before. This means you can create very complex licensing models and build systems that actually enforce those models.

If you want to learn more about using semantics to avoid legal fees and copyright compliance penalties, click here and provide your contact information. We will send you a link to my webinar “Managing Digital Rights Metadata with Semantic Technologies”, which should get you started down the right path.

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Demian Hess is Avalon Consulting, LLC's Director of Digital Asset Management and Publishing Systems. Demian has worked in online publishing since 2000, specializing in XML transformations and content management solutions. He has worked at Elsevier, SAGE Publications, Inc., and PubMed Central. After studying American Civilization and Computer Science at Brown University, he went on to complete a Master's in English at Oregon State University, as well as a Master's in Information Systems at Drexel University.

Comments

This is really interesting. In your graphs, have you invented your own ontology / data model? Are you aware of any existing ontologies / vocabularies for expressing these? It seems to me that it would really help reduce friction if everyone used the same language.

About Avalon Consulting, LLC

Avalon Consulting, LLC transforms data investments into actionable business results through the visioning and implementation of Big Data, Web Presence, Content Publishing, and Enterprise Search solutions. We are the trusted partner to over one hundred clients, primarily Global 2000 companies, public agencies, and institutions of higher learning.